Abstract

According to the brief and meaningless features of the social media content, we propose a text classification algorithm based on word vectors, which can quickly and effectively realize the automatic classification of the short text in social media. In view of the lack of word order and position considerations in the Word2vec model, we combine the Word2vec trained word vector with the convolutional neural network (CNN) model to propose SW-CNN and WW-CNN classification algorithms. The methods are evaluated on the three different datasets. Compared with existing text classification methods based on convolutional neural network (CNN) or recurrent neural network (RNN), the experimental results show that our approach has superior performance in short text classification.